Predictive value of statistical models.
A review is given of different ways of estimating the error rate of a prediction rule based on a statistical model. A distinction is drawn between apparent, optimum and actual error rates. Moreover it is shown how cross-validation can be used to obtain an adjusted predictor with smaller error rate. A detailed discussion is given for ordinary least squares, logistic regression and Cox regression in survival analysis. Finally, the splitsample approach is discussed and demonstrated on two data sets.
Van Houwelingen
J C
JC
Department of Medical Statistics, Leiden University, The Netherlands.
Le Cessie
S
S
D002138:Calibration; D005260:Female; D006085:Graft Survival; D006801:Humans; D016030:Kidney Transplantation; D016018:Least-Squares Analysis; D015233:Models, Statistical; D010051:Ovarian Neoplasms / Q000401:mortality; D011379:Prognosis; D012044:Regression Analysis; D016019:Survival Analysis